1. Field of the Invention
The present invention relates generally to an antenna device and a control method thereof, and in particular, to a smart antenna device and a control method thereof.
2. Description of the Related Art
In general, an antenna device receives a signal at a predetermined frequency. Antenna devices have been developed for long distance communications and are currently being used for base stations and terminals in mobile communication systems. Along with the rapid development of antennas, the concept of a smart antenna has been introduced. A smart antenna is an intelligent antenna that forms beams after signal processing for a particular purpose in a base station or a mobile terminal. It is expected that smart antennas will be adopted for IMT-2000 (International Mobile Telecommunicaiton-2000). The use of a smart antenna increases system performance.
The smart antenna has emerged as a solution to the problem of limited available frequency resources. Especially with low power, the smart antenna can exert the same performance as or higher performance than the existing systems. The smart antenna is operated on the principle that only a desired signal is extracted from interfering signals, that is, a high gain is given to a desired signal""s direction, and a low gain to the directions of the other signals to thereby enable a transmitting/receiving end to obtain more power with respect to the same transmission power. The smart antenna incurs constructive interference in the desired location and destructive interference elsewhere, which is called beamforming.
The smart antenna has three advantages on the whole.
First, signals are gathered to a desired location without distribution, thereby increasing gain. Therefore, a coverage area per base station becomes wider and the increase of gain reduces the power consumption of a mobile terminal, that is, the life of its battery is increased.
Secondly, since signals in undesired directions are effectively removed, interference can be cancelled. In particular, the interference canceling effect becomes great in a system susceptible to interference like CDMA (Code Division Multiple Access). A CDMA system then accommodates more subscribers in the case of voice communication and provides high rate data communication in the case of data communication.
Thirdly, the smart antenna also implements spatial filtering. Accordingly, multipath effects can be remarkably reduced.
There are two types of smart antennas depending on their beamformation methods: switched beam smart antennas and adaptive beam smart antennas. The former uses a fixed beam pattern and so may result in performance decrease if a user is disposed between antenna patterns. On the other hand, the latter uses an antenna pattern that varies with time or according to ambient environment, thus operating more adaptively to the environment than the former and can form a beam direct to a user.
The aim of a switched beam smart antenna is to detect the direction of a strong signal and select the signal from the direction.
Most of adaptive beamforming algorithms may be categorized into the following three classes or combinations of them.
Algorithms based on estimation of DOAs (Directions Of Arrival) of received signals.
In the DOA-estimation-based algorithms, the DOAs of received signals are first estimated and beams are then formed in the estimated directions. The techniques for DOA estimation include MUSIC (Multiple Signal Classification), Pisarenko, ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques), and ML (Maximum Likelihood). Beamformers operate by conventional beamforming and LCMV (Linear Constraint Minimum Variance).
Algorithms based on training sequence.
In these algorithms including SMI (Sample Matrix Inversion), LMS (Least Mean Square) and RLS (Recursive Least Square), a beam pattern is determined using a training sequence which is known to both the transmitter and the receiver. The training sequence based algorithms are easy to implement though limited, due to the use of a training sequence.
Blind adaptive algorithms.
Blind adaptive algorithms do not require a training sequence. Instead, they exploit some known properties of a desired received signal in determining a beam pattern. These blind adaptive algorithms include CMA (Constant Modulus Algorithm) and FA (Finite Alphabet) utilizing signal constellation, and a cyclo-stationary method and a high order statistic method based on oversampling characteristics. A disadvantage of these algorithms is complexity though they are free of overhead such as the use of a training sequence and constraints.
Many blind adaptive algorithms have been studied and suggested. Blind adaptive beamformation is done by spectral estimation or parameter estimation.
Major spectrum estimation methods are power maximization and LS-SCORE (Least Square-Spectral Self Coherence Restoral). Most of those methods are based on eigen decomposition. Especially, MCGM (Modified Conjugate Gradient Method) allows real time processing and has relatively good performance.
Major parameter estimation methods are ML (Maximum Likelihood) and ILSP (Iterative Least Square Projection). Despite its excellent performance, ML requires a considerable volume of computation. Meanwhile, ILSP markedly reduces the computation requirements inherent to the ML by iterating the least square solutions of ML, but it is not suitable for real time processing. The volume of required computation can be remarkably reduced by ILSP-CMA that employs ILSP along with CMA utilizing the constant envelope of a signal.
ILSP-CMA iteratively calculates solutions using the constant envelope of a signal. While it stably operates and has good performance, ILSP-CMA is not available to a signal that does not have a constant envelope. ILSP-CMA is the process of generating an Mxc3x97N input signal matrix for the input of N snapshots and processing the matrix as a block. This method causes latency and requires a great volume of instantaneous computation and large instantaneous memory capacity.
ILSP-CMA will be described below in more detail in connection with the structure of an adaptive array antenna.
FIG. 1 is a block diagram of a typical adaptive array antenna. Referring to FIG. 1, an array antenna 101 includes a set of M antennas, each antenna having the same characteristics. The antennas are uniformly spaced from each other by a distance d. xcex8k is the incident angle of a signal impinging on an antenna from a kth signal source Signals received at the array antenna 101 are fed to a pre-beamforming block 102. The pre-beamforming block 102, which is an optional block, performs coarse beamforming using the result of post-beamforming or preliminarily acquired information. A despreader 103 despreads the coarsely beamformed signals to reduce interference from the other signal sources and thus to facilitate signal processing. The despreader 103 is available only to a CDMA system. The despreader 103 may be disposed as shown in FIG. 1 or at the rear end of an adaptive array processing unit 105. M despread signals, Xxe2x96xa1to XM are applied to the input of the adaptive array processing unit 105.
The adaptive array processing unit 105 includes weight factor operators 108 to 110 for assigning weight factors to the M input signals, an adder 111 for summing the outputs of the weight factor operators 108 to 110, an error generator 107, and an adaptive algorithm processor 106. In operation, the output signal Ŝk of the adaptive array processing unit 105 and a reference signal Sk are fed to the error generator 107. The error generator 107 generates an error signal using the two input signals. The adaptive algorithm processor 106 calculates weight factors W1 to WM from the error signal by a predetermined algorithm. The weight factor operators 108 to 110 calculate the input signals X1 to XM with the weight factors W1 to WM. The adder 111 sums the calculated signals received from the weight factor operators 108 to 110.
A detailed description is made below of the operation of the adaptive array processing unit 105.
A signal received from K signal sources in total at an mth antenna among the M antennas of the array antenna 101 is calculated by                                           x            m                    ⁡                      (            t            )                          =                                            ∑                              k                =                1                            K                        ⁢                          xe2x80x83                        ⁢                                                            S                  k                                ⁡                                  (                  t                  )                                            ⁢                              ⅇ                                                      -                    j2π                                    ⁢                                                                                    (                                                  m                          -                          1                                                )                                            ⁢                      d                                        λ                                    ⁢                  sin                  ⁢                                      xe2x80x83                                    ⁢                                      θ                    k                                                                                +                                    v              m                        ⁡                          (              t              )                                                          (        1        )            
where Sk(t) is a signal from a kth signal source, xcex8k is the incident angle of the signal from the kth signal source, and vm(t) is Additive White Gaussian Noise (AWGN) added to the mth antenna. If the distance d between the antennas is xcex/1, equation (1) is changed to                                           x            m                    ⁡                      (            t            )                          =                                            ∑                              k                =                1                            K                        ⁢                          xe2x80x83                        ⁢                                                            S                  k                                ⁡                                  (                  t                  )                                            ⁢                              ⅇ                                                      -                                          jπ                      ⁡                                              (                                                  m                          -                          1                                                )                                                                              ⁢                  sin                  ⁢                                      xe2x80x83                                    ⁢                                      θ                    k                                                                                +                                    v              m                        ⁡                          (              t              )                                                          (        2        )            
Equations (1) and (2) a re be expressed in matrices for K signal sources and N snapshots as
X=AS+Vxe2x80x83xe2x80x83(3)
where X is an Mxc3x97N matrix of signals received at the array antenna 101, A is an Mxc3x97K steering matrix, S is a Kxc3x97N matrix of transmitted signals from the signal sources, and V is an Mxc3x97N noise matrix. Therefore,
X=[X(1)X(2) . . . X(n) . . . X(N)]
S=[S1S2 . . . Sk . . . SK]T
Ak=[A1A2 . . . Ak . . . AK]xe2x80x83xe2x80x83(4)
where X(n) is a signal received at an nth antenna, Sk is a transmission signal from a kth signal source, and Ak is a kth steering vector, which can be also expressed as
xe2x80x83X(n)=[x1(n)x2(n) . . . xm(n) . . . xM(n)]T
Sk=[Sk(1)Sk(2) . . . Sk(n) . . . Sk(N)]
Ak=[ak(1)ak(2) . . . ak(m) . . . ak(M)]T
=[1exe2x88x92jxcfx80sin xcex8k . . . exe2x88x92jxcfx80(mxe2x88x921)sin xcex8k . . . exe2x88x92jxcfx80(Mxe2x88x921)sin xcex8k ]Txe2x80x83xe2x80x83(5)
In the case of a CDMA system, signals from undesired signal sources are removed through despreading in the despreader 103 and so the system can be simply modeled to have a particular kth signal source. Accordingly, S and A in equation (5) reduce to
S≈Sk, for CDMA system after despreading
A≈Ak, for CDMA system after despreadingxe2x80x83xe2x80x83(6)
The adaptive array antenna system obtains the steering matrix A having an optimal solution using equation (4) in the adaptive array processing unit 105 of FIG. 1. For X=AS, only the input signal matrix X in equation (4) is known. MA estimation is usually used to estimate the transmission matrix S and the steering matrix A. The ML estimation is to minimize the cost function F(A, S) of equation (7) to equation (8).
F(A,S:X)=∥Xxe2x88x92AS∥F2xe2x80x83xe2x80x83(7)
A,Smin=∥Xxe2x88x92AS∥F2xe2x80x83xe2x80x83(8)
where ∥xe2x80xa2∥F2 is a squared Frobenius form. A major method to iterate the optimal solution of equations (7) and Eq. (8), ILSP-CMA will be described with reference to FIG. 2.
FIG. 2 is a flowchart illustrating an ILSP-CMA method for obtaining an optimal solution to a squared Frobenius form.
The ILSP-CNA calculates solutions iteratively to achieve optimal A and S from the input signal matrix X in equations.(7) and (8) on the assumption that the matrix S has a constant envelope.
Referring to FIGS. 1 and 2, an iteration coefficient i is set to 0 and the steering matrix A is set to its initial value A0 in step 200. The adaptive array antenna system waits until N snapshots are received to generate the input signal matrix X in step 202. Upon receipt of N snapshots, the iteration coefficient i is increased by 1 in step 204 and a transmission signal matrix Si that minimizes F(A, S:X) is obtained by computing a least square solution using a steering matrix Aixe2x88x921 in step 206 as follows.
Si=((Aixe2x88x921)HAixe2x88x921)xe2x88x921(Aixe2x88x921)HXxe2x80x83xe2x80x83(9)
In equation (9) and hereinbelow, ( )H denotes a Hermitian operation.
Since the transmission signals have constant envelopes, the transmission signal matrix Si is mapped to the nearest points on a unit circle. After the mapping, in step 208 a steering matrix Ai that minimizes F(A, S:X) is obtained by calculating a least square using the transmission signal matrix Si by
Ai=X(Si)H(Si(Si)H)xe2x88x921xe2x80x83xe2x80x83(10)
and the elements of each column in the steering matrix Ai are normalized by dividing them by the first element in each column vector in the steering matrix A1. It is determined whether the steering matrix Ai has converged in step 210. If the steering matrix Ai has converged, the iteration is terminated and the procedure goes to step 212. Otherwise, the procedure returns to step 204. In step 212, data is modulated using the transmission signal matrix S, or a weight factor W is calculated using the steering matrix A and the transmission signal matrix S is computed for data modulation.
The above-described ILSP-CMA method iteratively calculates solutions using the constant envelope of a signal. Therefore, it operates stably in a co-channel interference environment and has good performance.
The ILSP-CMA method, however, is not applicable to a signal that does not have a constant envelope. Moreover, Mxc3x97N input signals must be generated in an Mxc3x97N matrix after N snapshots are received and the Mxc3x97N signals are processed as a unit block. This implies that a memory of at least a block size must be prepared for block processing. When an input block is processed after its reception, much instantaneous volume of computation is required. Also, a memory for accommodating the computation requirement is needed.
It is, therefore, an object of the present invention to provide a smart antenna device that offers stable and good performance irrespective of constant envelope characteristics, and a control method thereof.
It is another object of the present invention to provide a smart antenna device that can process data in real time and a method thereof.
It is a further object of the present invention to provide a smart antenna device implemented with a reduced memory capacity and requiring a great volume of computation, and a method thereof.
The above and other objects of the present invention are achieved by providing adaptive beamforming algorithms for an adaptive array smart antenna. According to one aspect of the present invention, the adaptive array smart antenna constructs an input signal matrix with a predetermined number of input signals, computes a transmission signal matrix that minimizes a cost function using a first steering matrix set to an initial value and the input signal matrix, computes a second steering matrix that minimizes the cost function using the transmission signal matrix and the input signal matrix, maps the second steering matrix on a unit circle, constructs a third steering matrix with the mapped values, normalizes the third steering matrix and determining whether the third steering matrix converges, and demodulates data using the transmission signal matrix if the third steering matrix converges.